ComfyUI-LightVAE

ComfyUI-LightVAE

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High-Performance VAE Custom Nodes

English | 简体中文

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📖 Introduction

ComfyUI-LightVAE is a collection of LightX2V VAE custom nodes designed for ComfyUI, supporting high-performance video VAE models including LightVAE and LightTAE.

The LightX2V team has deeply optimized VAE, creating two major series: LightVAE and LightTAE, which significantly reduce memory usage and improve inference speed while maintaining high quality.

✨ Key Features

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🎯 LightVAE Series

Feature: Best Balance ⚖️

• ✅ Uses Causal 3D Conv (same as official)
• ✅ Near-official quality ⭐⭐⭐⭐
• ✅ ~50% less memory (~4-5 GB)
• ✅ 2-3x faster
• ✅ Balances quality, speed, and memory 🏆

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⚡ LightTAE Series

Feature: Ultra-fast + High Quality 🏆

• ✅ Minimal memory usage (~0.4 GB)
• ✅ Lightning-fast inference
• ✅ Near-official quality ⭐⭐⭐⭐
• ✅ Surpasses open-source TAE

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🚀 Performance Comparison

Test Environment: H100 GPU, BF16, 81-frame video (480P)

| Model | Encode Time | Decode Time | Encode Memory | Decode Memory | Quality | |:------|:------------|:------------|:--------------|:--------------|:--------| | lightvaew2_1 | 1.5s | 2.1s | 4.8GB | 5.6GB | ⭐⭐⭐⭐⭐ | | lighttaew2_1 | 0.4s | 0.25s | 0.009GB | 0.4GB | ⭐⭐⭐⭐ | | Wan2.1_VAE | 4.2s | 5.5s | 8.5GB | 10.1GB | ⭐⭐⭐⭐ | | taew2_1 | 0.4s | 0.25s | 0.009GB | 0.4GB | ⭐⭐⭐ |

Performance Improvements:

• 🚀 LightVAE is 2-3x faster than official VAE, 50% less memory
• ⚡ LightTAE is 10+ times faster than official VAE, 95%+ less memory
• 🎨 Near-official VAE quality, surpasses open-source TAE

📦 Installation

1. Install LightX2V Dependencies

vae requirements windows example

...comfy\python_embeded\python.exe -s -m pip install -r requirements.txt

# Clone LightX2V repository
git clone https://github.com/ModelTC/LightX2V
cd LightX2V

python setup_vae.py install
# windows example
...comfy\python_embeded\python.exe setup_vae.py install

2. Install ComfyUI-WanVideoWrapper

LightVAE nodes depend on WanVideoWrapper for main model support:

cd ComfyUI/custom_nodes
git clone https://github.com/kijai/ComfyUI-WanVideoWrapper

3. Install ComfyUI-LightVAE

cd ComfyUI/custom_nodes
git clone https://github.com/YOUR_USERNAME/ComfyUI-LightVAE

4. Restart ComfyUI

📥 Download Models

Main Models (Diffusion Models)

Option 1: Distilled Models (Recommended, 4-step)

• 🔗 Wan2.1 Distilled Models and Wan2.2 Distilled Models
• ✅ Supports BF16 format
• ✅ Supports FP8 format (requires models with _comfyui.safetensors suffix)

Option 2: Original Models (20-step)

• 🔗 Wan2.1 Official Models and Wan2.2 Official Models
• ✅ Supports BF16 format
• ✅ Supports FP8 format (requires models with _comfyui.safetensors suffix)

# Download to ComfyUI/models/diffusion_models/
huggingface-cli download lightx2v/Wan2.1/2-Distill-Models \
    --local-dir ./ComfyUI/models/diffusion_models/

VAE Models

All VAE Models (Required):

• 🔗 LightX2V Autoencoders

# Download all VAE models
huggingface-cli download lightx2v/Autoencoders \
    --local-dir ./ComfyUI/models/vae/

# Or download only what you need (Recommended)
huggingface-cli download lightx2v/Autoencoders lightvaew2_1.pth \
    --local-dir ./ComfyUI/models/vae/

Supported VAE Models:

• Wan2.1_VAE.pth / .safetensors - Official VAE 2.1
• Wan2.2_VAE.pth / .safetensors - Official VAE 2.2
• lightvaew2_1.pth / .safetensors - Optimized VAE 2.1 ⭐ Recommended
• taew2_1.pth / .safetensors - Open-source TAE 2.1
• taew2_2.pth / .safetensors - Open-source TAE 2.2
• lighttaew2_1.pth / .safetensors - Optimized TAE 2.1 ⚡ Fastest
• lighttaew2_2.pth / .safetensors - Optimized TAE 2.2

🎯 Node Documentation

1. LightX2V VAE Decoder Loader

Input Parameters:

• vae_filename - VAE model filename (automatically lists from ./models/vae/)
• dtype - Data type (bfloat16 / float16 / float32)
• device - Compute device (cuda / cpu)

Output:

• vae - VAE model object

Features:

• ✅ Automatically identifies VAE type from filename
• ✅ Supports all LightX2V VAE models

2. LightX2V VAE Decode

Input Parameters:

• vae - VAE object from Loader
• latent - Latent representation

Output:

• IMAGE - Decoded video frames

Supports:

• ✅ All VAE series (WanVAE, LightVAE)
• ✅ All TAE series (TAE, LightTAE)

🖼️ Example Workflows

Wan2.1 I2V 4-step FP8 + LightVAE

High-performance configuration using 4-step distilled model + LightVAE optimized decoder.

Workflow File: example/workflows/wan2.1_I2V_4step_fp8_lightvae.json

Wan2.2 TI2V + LightVAE

Wan2.2 Text-Image-to-Video + LightVAE decoding.

Workflow File: example/workflows/wan2.2_TI2V_lightvae.json

⚠️ Important Notes

Model Compatibility

• ⚠️ Wan2.1 VAE can only be used with Wan2.1/Wan2.2-A1B backbone models
• ⚠️ Wan2.2 VAE can only be used with Wan2.2 TI2V backbone models
• ❌ Do not mix different versions of VAE and backbone models

📚 Related Resources

• Project Homepage: https://github.com/ModelTC/LightX2V
• VAE Models: https://huggingface.co/lightx2v/Autoencoders
• Video Generation Models: https://huggingface.co/lightx2v/
• ComfyUI-WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper
• TAE Series Models: https://github.com/madebyollin/taesd
• Wan-AI: https://huggingface.co/Wan-AI

🙏 Acknowledgements

If this project helps you, please give a ⭐ to LightX2V and this repository!

📞 Support

• GitHub Issues: Issues page of this repository
• LightX2V Issues: https://github.com/ModelTC/LightX2V/issues
• HuggingFace: https://huggingface.co/lightx2v

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Enjoy using LightX2V VAE! 🚀

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